Deckgl Charts

Choropleth Chart

deckgl. choropleth ( x , color_column , elevation_column=None , color_aggregate_fn='count' , color_factor=1 , elevation_aggregate_fn='sum' , elevation_factor=1 , data_points=100 , add_interaction=True , width=800 , height=400 , step_size=None , step_size_type=<class 'int'> , geoJSONSource=None , geoJSONProperty=None , geo_color_palette=None , mapbox_api_key=None , map_style='dark' , tooltip=True , **library_specific_params )

Parameters

x: str

x-axis column name from the gpu dataframe

color_column: str

column name from the gpu dataframe on which color palettes are based on

elevation_column: str | Optional

column name from the gpu dataframe on which elevation scale is based on

color_aggregate_fn: {‘count’, ‘mean’, ‘sum’, ‘min’, ‘max’, ‘std’},

default “count”

aggregate function to be applied on the color column while performing groupby aggregation by column x

color_factor: float, default 1

factor to be multiplied to each value of color column before mapping the color

elevation_aggregate_fn: {‘count’, ‘mean’, ‘sum’, ‘min’, ‘max’, ‘std’},

default “count”

aggregate function to be applied on the elevation column while performing groupby aggregation by column x

elevation_factor: float, default 1

factor to be multiplied to each value of elevation column before scaling the elevation

data_points: int, default 100

add_interaction: {True, False}, default True

width: int, default 800

height: int, default 400

step_size: int, default 1

step_size_type: {int, float}, default int

geoJSONSource: str

url to the geoJSON file

geoJSONProperty: str, optional

Property to use while doing aggregation operations using the geoJSON file. Defaults to the first value in properties in geoJSON file.

geo_color_palette: bokeh.palette, default bokeh.palettes.Inferno256

nan_color: str, default white

color of the patches of value NaN in the map.

mapbox_api_key: str, default os.getenv(‘MAPBOX_API_KEY’)

map_style: {‘dark’, ‘light’}, default ‘dark’

map background type

tooltip: {True, False}, default True

title: str,

chart title

**library_specific_params:

additional library specific keyword arguments to be passed to the function

Returns

A bokeh chart object of type 3dchoropleth

Example 3d-Choropleth

import numpy as np
import cudf
import cuxfilter

geoJSONSource='https://raw.githubusercontent.com/rapidsai/cuxfilter/GTC-2018-mortgage-visualization/javascript/demos/GTC%20demo/src/data/zip3-ms-rhs-lessprops.json'
size = 1000

cux_df = cuxfilter.DataFrame.from_dataframe(
    cudf.DataFrame({
                    'color':np.random.randint(20,30, size=size*10)/100,
                    'zip': list(np.arange(1,1001))*10,
                    'elevation': np.random.randint(0,1000, size=size*10)
    })
)

chart0 = cuxfilter.charts.choropleth( x='zip', color_column='color', color_aggregate_fn='mean',
            elevation_column='elevation', elevation_factor=1000, elevation_aggregate_fn='mean',
        geoJSONSource=geoJSONSource, data_points=size, add_interaction=True
)

#declare dashboard
d = cux_df.dashboard([chart0],theme = cuxfilter.themes.dark, title='Mortgage Dashboard')

# cuxfilter.load_notebook_assets()
chart0.view()

Example 2d-Choropleth

import numpy as np
import cudf
import cuxfilter

geoJSONSource='https://raw.githubusercontent.com/rapidsai/cuxfilter/GTC-2018-mortgage-visualization/javascript/demos/GTC%20demo/src/data/zip3-ms-rhs-lessprops.json'
size = 1000

cux_df = cuxfilter.DataFrame.from_dataframe(
    cudf.DataFrame({
                    'color':np.random.randint(20,30, size=size*10)/100,
                    'zip': list(np.arange(1,1001))*10,
                    'elevation': np.random.randint(0,1000, size=size*10)
    })
)

chart0 = cuxfilter.charts.choropleth( x='zip', color_column='color', color_aggregate_fn='mean',
        geoJSONSource=geoJSONSource, data_points=size, add_interaction=True
)

#declare dashboard
d = cux_df.dashboard([chart0],theme = cuxfilter.themes.dark, title='Mortgage Dashboard')

# cuxfilter.load_notebook_assets()
chart0.view()